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Record W7098742124

To be submitted to the Astrophysical Journal Preprint typeset using LATEX style emulateapj v. 5/2/11 THE WEAK LENSING SIGNAL AND THE CLUSTERING OF BOSS GALAXIES: COSMOLOGICAL CONSTRAINTS

2016· article· en· W7098742124 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Conservation and Criminology Analyses
Canadian institutionsnot available
Fundersnot available
KeywordsCMB cold spotGalaxyWeak gravitational lensingCosmologyHaloRedshiftDark matterSkyCluster analysis
DOInot available

Abstract

fetched live from OpenAlex

To be submitted to the Astrophysical Journal We perform a joint analysis of the abundance, the clustering and the galaxy-galaxy lensing signal of galaxies from Data Release 11 of the Sloan Digital Sky Survey III Baryon Oscillation Spectroscopic Survey. We fit halo occupation parameters and cosmological parameters (Ωm and σ8) to both of these observables simultaneously, and thus break the degeneracy between galaxy bias and cosmology. The cosmological analysis is the first of its kind to be performed at a redshift as high as 0.53. We present measurements of the clustering signal of galaxies by utilizing various stellar mass threshold samples. The galaxy-galaxy weak lensing signal is obtained by using the shape catalog of background galaxies from the Canada France Hawaii Telescope Legacy Survey, which was made publicly available by the CFHTLenS collaboration, with an area overlap of about 100 deg2. We analyze these measurements in the framework of the halo model. Adopting a flat ΛCDM cosmology with priors on Ωbh2, ns and h from the analysis of WMAP 9-year data, we obtain Ωm = 0.310+0.019−0.020 and σ8 = 0.785 +0.044 −0.044 (68 % confidence) after marginalizing over the halo occupation distribution parameters and a number of other

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.358
Threshold uncertainty score0.916

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0050.007
Science and technology studies0.0010.001
Scholarly communication0.0090.002
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.3580.270

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.272
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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